SkyVision Platform Achieves Early and Precise Identification of Smoke and Open Fire

In the current trend of AI empowering people's livelihoods, the field of smart security is constantly exploring and innovating. Behavior recognition technology plays an important role in emergency security. The Tianyan SkyVision platform of WeLinkirt brings a new solution for the early identification of smoke and open fire.

In terms of the user scenario, a large industrial park faces the threat of fire hazards during daily production and operation. Various workshops, warehouses and other places in the park are key monitoring areas. The detection objects are mainly the early signs of smoke and open fire. Once these situations occur, they may spread rapidly, causing serious property losses and casualties.

The pain points are that the traditional detection methods for smoke and open fire have many quantitative dilemmas. On the one hand, the miss rate is relatively high, about 3%. This means that some early signs of smoke and open fire cannot be detected in time, increasing the risk of fire. On the other hand, false alarms occur frequently, with a false alarm rate of 20%. This not only wastes a lot of manpower to verify the situation, but also may cause the staff to become numb to the alarms. Against the background of AI empowering people's livelihoods, traditional technologies can no longer meet the innovation and development needs of behavior recognition technology in smart security.

Technical Principle

The Tianyan SkyVision 0-code video surveillance AI platform uses advanced computer vision algorithms and deep learning technologies. Through learning a large amount of smoke and open-fire image data, the platform can accurately identify the characteristics of smoke and open fire. Its hardware foundation is a high-performance edge box, which can process surveillance video data in real-time. The platform is effective because it utilizes the semantic understanding ability of the DaoAI World model, which can accurately extract the semantic information of smoke and open fire from complex scenes and avoid misjudgments caused by environmental interference.

• Computer vision algorithm: Extract and analyze features from video images to identify the shape, color and other features of smoke and open fire.
• Deep learning technology: Train the model with a large amount of data to improve the accuracy and robustness of recognition.
• Edge box: Process data in real-time, reduce data transmission delay and achieve rapid alarm.
• DaoAI World model: Provide semantic understanding and enhance the adaptability to complex scenes.

WeLinkirt Solution and Product

Centered on the Tianyan SkyVision platform, this platform supports on - site self-model training within hours. Enterprises can quickly train a suitable recognition model according to the actual situation of their own parks. In terms of behavior/event recognition, it can accurately identify the early signs of smoke and open fire. The real-time alarm function of the edge box can immediately issue an alarm once an abnormal situation is detected. Moreover, the platform ensures that 100% of local data does not leave the site, ensuring data security. At the same time, combined with the semantic understanding ability of the DaoAI World model, the recognition accuracy is further improved.

The Tianyan SkyVision platform provides an efficient and accurate early-identification solution for smoke and open fire in emergency security.

The quantitative results are remarkable. First of all, the detection rate has increased from the original 97% to 99.4%, greatly improving the ability to detect early signs of smoke and open fire. Secondly, the false alarm rate has been reduced by -70%, from the original 20% to 6%, reducing a large amount of unnecessary manpower input. In addition, due to the timely detection of early hidden dangers, the shutdown time of the park caused by fires has been significantly reduced, saving huge economic losses for the enterprise.